Robot Manipulator Calibration Using a Model Based Identification Technique and a Neural Network With the Teaching Learning-Based Optimization

Abstract

This paper proposes a new calibration method for enhancing robot positional accuracy of the industrial manipulators. By combining the joint deflection model with the conventional kinematic model of a manipulator, the geometric errors and joint deflection errors can be considered together to increase its positional accuracy. Then, a neural network is designed to additionally compensate the unmodeled errors, specially, non-geometric errors. The teaching-learning-based optimization method is employed to optimize weights and bias of the neural network. In order to demonstrate the effectiveness of the proposed method, real experimental studies are carried out on HH 800 manipulator. The enhanced position accuracy of the manipulator after the calibration confirms the feasibility and more positional accuracy over the other calibration methods.

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

“…The TLBA has been previously applied in an efficient way for several engineering optimization problems [28]. Some examples of these successful implementations are reactive power control in electrical systems [29], service restoration in distribution feeders [30], Tsallis-entropy-based feature selection classification [31], generation expansion-planning problem [32], design of passive filters [33], dissimilar resistance spot-welding process [34], water supply pipe condition prediction [35], robot manipulator calibration [36], harmonic elimination in multi-level inverters [37], operation analysis of a grid-connected photovoltaic (PV) with battery system [38] and parameter extraction of PV modules [39,40]. The abovementioned advantages of the TLBA and its successful applications in a wide array of engineering problems are the main reasons for the selection of the TLBA in this article.…”

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

“…• Kinematic errors: Kinematic errors are related to and have direct impact on the kinematic model of the robot [110], [111]. These may be due to manufacturing and assembly tolerances, geometry of the robot components such as orthogonality or parallelism or the position of the reference frame.…”

Online Measuring of Robot Positions Using Inertial Measurement Units, Sensor Fusion and Artificial Intelligence

“…The second approach is known as non-parametric [? ], where the robot model is submitted to a nonlinear regression equation or other methods, such as the case of neural networks [12], [16], [17].…”